Need of Tokenization

Tokenization is a crucial step in text processing and natural language processing (NLP) for several reasons.

• Effective Text Processing: Tokenization reduces the size of raw text so that it can be handled more easily for processing and analysis.
• Feature extraction: Text data can be represented numerically for algorithmic comprehension by using tokens as features in machine learning models.
• Language Modelling: Tokenization in NLP facilitates the creation of organized representations of language, which is useful for tasks like text generation and language modelling.
• Information Retrieval: Tokenization is essential for indexing and searching in systems that store and retrieve information efficiently based on words or phrases.
• Text Analysis: Tokenization is used in many NLP tasks, including sentiment analysis and named entity recognition, to determine the function and context of individual words in a sentence.
• Vocabulary Management: By generating a list of distinct tokens that stand in for words in the dataset, tokenization helps manage a corpus’s vocabulary.
• Task-Specific Adaptation: Tokenization can be customized to meet the needs of particular NLP tasks, meaning that it will work best in applications such as summarization and machine translation.
• Preprocessing Step: This essential preprocessing step transforms unprocessed text into a format appropriate for additional statistical and computational analysis.

Tokenization in natural language processing (NLP) is a technique that involves dividing a sentence or phrase into smaller units known as tokens. These tokens can encompass words, dates, punctuation marks, or even fragments of words. The article aims to cover the fundamentals of tokenization, it’s types and use case.